code: def on_before_optimizer_step(self, optimizer, *_):
      self.check_grads_unscaled(optimizer)
      # manually clip
      self.clipped_parameters = []
      for p in self.parameters():
          copy = p.detach().clone()
          copy.grad = p.grad.clone()
          self.clipped_parameters.append(copy)
      clip_val = self.trainer.gradient_clip_val
      torch.nn.utils.clip_grad_value_(self.clipped_parameters, clip_val)
  
imports: import os
  import re
  from unittest import mock
  import pytest
  import torch
  from lightning.pytorch import Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel
  from lightning.pytorch.plugins import MixedPrecision
  from torch import Tensor
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 4 for calling the TestPrecisionModel obj's underlying member method code with example usage: obj.on_before_optimizer_step(optimizer) without return types